Slot Fillers

Slot Fillers let you monitor every user input for specific Slot matches and store them in the Context object. This way, if an answer is already provided, the AI Agent skips the corresponding question in the Question Node. For example, if a user says I'm flying from San Francisco on United and the AI Agent asks What city are you departing from? — this question would be redundant and create a poor user experience. Instead, Slot Fillers ensure that only missing information is requested.

Key Benefits

Collecting and Remembering Information. With Slot Fillers, all required data is gathered and retained throughout the conversation, reducing errors and making conversations with the AI Agent smoother and more natural.
Handling Different Input Types. Slot Fillers accept various formats, for example, dates and numbers, making it flexible for user inputs.

To learn more about the benefits of using Slot Fillers and how they work, watch this video:

Restrictions

Slot Fillers fail when multiple values share the same Slot type, making it impossible to distinguish between them. For example, I'm flying from SFO to ORD on United. The AI Agent can’t distinguish between departure airport and arrival airport because both match airport_code. To avoid this restriction, you can:
- Create separate Slot Fillers for each value:
  - Departure Airport — use a Context Key, for example, departure_city.
  - Arrival Airport — use a Context Key, for example, arrival_city.
- Modify the Flow to ask distinct questions for each Slot instead of extracting both from a single user input.

Working with Slot Fillers

You can create, edit, and delete Slot Fillers in NLU > Slot Fillers in the Flow editor.

Slot Filler Settings

Required Fields

Field	Description	Example
Name	The display name of the Slot Filler, used for identification.	`Departure Airport Slot`
Type	Defines the type of input expected. You can select from the following Slot types: - Slot – a Lexicon Slot. Make sure a Lexicon is attached and that this Slot is included in it. - Regular Expression – a Regex Slot. - Intent – extracts values based on recognized Intents. Useful when Slot filling is tied to Intent classification. - System Slot – predefined Slot types that automatically extract common entity types such as dates, numbers, temperatures, and more.	-
Slot Name	This parameter appears if the Slot type is selected. Specifies which Slot this Filler is associated with.	`airport_code`, `date`, `name`
Regular Expression	This parameter appears if the Regular Expression type is selected. Enter the regular expression to extract specific patterns from user input. Regex expressions must start and end with `/`. Flags like `g` for global matching aren’t supported. Example: `/^\d{3}-\d{3}-\d{4}$/`.
Context Key	The variable name used to store the extracted value in the Context object.	`departure_city`, `arrival_date`

Optional Fields

Field	Description	Example
Additional Validation	Allows extra validation rules to ensure correct formatting or values.	Ensure date format is `YYYY-MM-DD`.
Result Location	Determines where to extract the Slot value from. By default, the Slot value is extracted from the user input. You can set a CognigyScript expression in this parameter to override the detected Slot value with a value from the Input, Context, or Profile objects.	`{{input.bookingReference}}`
Store result in Context	Stores the extracted value in the Context object.	`"departure_city": "SFO"`.
Store result in Contact Profile	Stores the extracted value in the Contact Profile for future interactions.	Store “preferred_airline” for returning users.
Store Detailed Results	Stores metadata about the extracted Slot, for example, confidence scores.	Confidence score of `0.92` for `"ORD"`.
Use Positive Keyphrases Only	Limits Slot extraction to predefined keyphrases.	Only accept `"San Francisco"` for `departure_city`.
Skip if the result is already in Context	Prevents asking for the Slot again if a valid value already exists in the Context object.	If `"departure_city": "LAX"` is set, don’t ask again.

Example

Step 1. Add Slots

Create a new Flow called Slot Fillers.
Navigate to the NLU tab and attach relevant Lexicons, for example, airports and airlines.
On the Slot Fillers tab, follow these steps:
1. Create a new Slot Filler named Airline.
2. Select Slot from the Type list.
Enter airline in both the Slot name and Context Key fields.
Repeat the steps 3.1 and 3.2 to create another Slot Filler named Airport, enter airport_code in the Slot name and Context Key fields.
Click Build Model.

Step 2. Configure Question Nodes

Create a Question Node, enter What is your departure airport? in the Text field and select Slot from the Question Type list.
Enter airport_code in Slot Name and Context Key to use.
Enable Skip if answer exists in Context.
Repeat the process for What is your airline?, enter airline in the Slot name and Context Key fields.

Step 3. Test Slot Fillers

Provide all information at once:
User Input: I'm leaving San Francisco on United.
Result: No additional questions are asked.

Provide partial information:
User Input: I'm flying on United.
AI Agent: What is your departure airport?
User Input: SFO
Result: The AI Agent skips asking for the airline.

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Overview

Overview

AI Agents

Human Escalation

Platform Features

For Developers

Administration and Installation

Key Benefits

Restrictions

Working with Slot Fillers

Slot Filler Settings

Example

More Information

​Key Benefits

​Restrictions

​Working with Slot Fillers

​Slot Filler Settings

​Example

​More Information

Key Benefits

Restrictions

Working with Slot Fillers

Slot Filler Settings

Example

More Information